New Methods in Iris Recognition John Daugman

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 37, NO. 5, OCTOBER 2007 1167 New Methods in Iris Recognition John Daugman Abstract—This paper presents the following four advances in presenting to the camera (e.g., nystagmus or deviated gaze). iris recognition: 1) more disciplined methods for detecting and The demands against false non-matches are also being raised faithfully modeling the iris inner and outer boundaries with active by the development of more tolerant and fluid user interfaces, contours, leading to more flexible embedded coordinate systems; 2) Fourier-based methods for solving problems in iris trigonome- which aim to replace the “stop-and-stare” camera interface with try and projective geometry, allowing off-axis gaze to be handled iris recognition on the move, off-axis, and at a distance [9]. by detecting it and “rotating” the eye into orthographic perspec- These two trends seem to require, paradoxically, that de- tive; 3) statistical inference methods for detecting and excluding cision criteria be used which are simultaneously much more eyelashes; and 4) exploration of score normalizations, depending conservative and liberal than those that are presently deployed. on the amount of iris data that is available in images and the required scale of database search. Statistical results are presented The purpose of this paper is to present four new advances in based on 200 billion iris cross-comparisons that were generated iris recognition that aim to simultaneously improve at both from 632 500 irises in the United Arab Emirates database to extremes. Sections II–IV present new methods of image analyze the normalization issues raised in different regions of processing for iris segmentation, allowing flexible shapes and receiver operating characteristic curves. coordinate systems. The images used in these sections come Index Terms—Active contours, biometrics, Gabor wavelets, from the National Institute of Standards and Technology gaze correction, iris recognition, score normalization, texture. (NIST) “Iris Challenge Evaluation” (ICE-1), and their file numbers are cited to facilitate comparative work. Sections V I. INTRODUCTION and VI analyze alternative score normalization rules that are adapted for differently sized databases, using results from HE ANTICIPATED large-scale applications of biometric 200 billion iris cross-comparisons. T technologies such as iris recognition are driving innovations at all levels, ranging from sensors to user interfaces, to algorithms and decision theory. At the same time as these II. ACTIVE CONTOURS AND GENERALIZED COORDINATES good innovations, possibly even outpacing them, the demands on the technology are getting greater. Today, many countries Iris recognition begins with finding an iris in an image, de- are considering or have even announced procurement of bio- marcating its inner and outer boundaries at the pupil and sclera, metrically enabled national identity (ID) card schemes, one of detecting the upper and lower eyelid boundaries if they occlude, whose purposes will be to detect and prevent multiple IDs. and detecting and excluding any superimposed eyelashes or Achieving that purpose will require, at least at the time when reflections from the cornea or eyeglasses. These processes may cards are issued and IDs are registered, an offline “each-against- collectively be called segmentation. Precision in assigning the all” cross-comparison. In effect then the number of biometric true inner and outer iris boundaries, even if they are partly comparisons that must be performed scales as the square of the invisible, is important because the mapping of the iris in a population. The decision confidence levels that will be required dimensionless (i.e., size invariant and pupil dilation invariant) to keep the false match rate (FMR) negligible, despite such vast coordinate system is critically dependent on this. Inaccuracy in numbers of opportunities to make false matches, can only be the detection, modeling, and representation of these boundaries described as astronomical. can cause different mappings of the iris pattern in its extracted At the other end of the conceptual receiver operating char- description, and such differences could cause failures to match. acteristic (ROC) curve from the FMR =0limit, much greater It is natural to start by thinking of the iris as an annulus. demands are also being placed on the false non-match rate Soon, one discovers that the inner and outer boundaries are (FnMR). This is partly because national-scale deployments usually not concentric. A simple solution is then to create must be as inclusive as possible; hence, it is unacceptable a nonconcentric pseudo-polar coordinate system for mapping to exclude members of outlier populations who, for various the iris, relaxing the assumption that the iris and pupil share reasons, may have a nonstandard eye appearance (e.g., non- a common center and requiring only that the pupil is fully round iris, coloboma, oddly shaped pupil, drooping eyelids, contained within the iris. This “doubly dimensionless pseudo- or much eyelash occlusion) or who simply have difficulty polar coordinate system” was the basis of my original paper on iris recognition [2] and patent [3], and this iris coordinate system was incorporated into International Organization for Standardization (ISO) Standard 19794-6 for iris data [7]. Soon Manuscript received September 20, 2006. This paper was recommended by one also discovers that, often, the pupil boundary is noncircular, Guest Editor K. W. Bowyer. and usually, the iris outer boundary is noncircular. Performance The author is with the Computer Laboratory, University of Cambridge, CB3 0FD Cambridge, U.K. in iris recognition is significantly improved by relaxing both Digital Object Identifier 10.1109/TSMCB.2007.903540 of those assumptions, replacing them with more disciplined 1083-4419/$25.00 © 2007 IEEE 1168 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 37, NO. 5, OCTOBER 2007 Fig. 1. Active contours enhance iris segmentation, because they allow for Fig. 2. Active contours enhance iris segmentation, because they allow for noncircular boundaries and enable flexible coordinate systems. The box in noncircular boundaries and enable flexible coordinate systems. The box in the lower left-hand corner shows curvature maps for the inner and outer iris the lower left-hand corner shows curvature maps for the inner and outer iris boundaries, which would be flat and straight if they were circles. Here, the outer boundaries, which would be flat and straight if boundaries were circles. Here, boundary (upper plot) is particularly noncircular. Dotted curves in the box and the pupil boundary (lower plot) is particularly noncircular. Dotted curves in the on the iris are Fourier series approximations. This iris is ICE-1 file 239261. box and on the iris are Fourier series approximations. This iris is ICE-1 file 240461. methods for faithfully detecting and modeling those boundaries whatever their shapes are and defining a more flexible and level for each snake is the image gradient in the radial direction. generalized coordinate system on their basis. Thus, the relative thickness of each snake roughly represents the Because the iris outer boundary is often partly occluded by sharpness of the corresponding radial edge. If an iris boundary eyelids, and the iris inner boundary may be partly occluded by were well-described as a circular edge, then the corresponding reflections from illumination, and sometimes both boundaries snake in its box should be flat and straight. In general, this is also by reflections from eyeglasses, it is necessary to fit flex- not the case. ible contours that can tolerate interruptions and continue their The dotted curve that is plotted within each snake, as well trajectory across them on a principled basis, which is somehow as superimposed on the corresponding loci of points in the iris driven by the data that exist elsewhere. A further constraint is image, is a discrete Fourier series approximation to the data. that both the inner and outer boundary models must form closed (In both figures, detected eyelid occlusions are also demarcated curves. A final goal is that we would like to impose a constraint by white pixels, and they interrupt the corresponding outer on smoothness based on the credibility of any evidence for boundary data snake, although the estimated contour continues nonsmooth curvature. through such interruptions.) The estimation procedure is to An excellent way to achieve all of these goals is to describe compute a Fourier expansion of the N regularly spaced angular the iris inner and outer boundaries in terms of “active contours” samples of radial gradient edge data {rθ} for θ =0 to θ = based on discrete Fourier series expansions of the contour N − 1.AsetofM discrete Fourier coefficients {Ck},fork =0 data. By employing Fourier components whose frequencies to k = M − 1, is computed from the data sequence {rθ} as are integer multiples of 1/(2π), closure, orthogonality, and follows: completeness are ensured. Selecting the number of frequency components allows control over the degree of smoothness N−1 −2πikθ/N that is imposed and over the fidelity of the approximation. In Ck = rθe . (1) essence, truncating the discrete Fourier series after a certain θ=0 number of terms amounts to low-pass filtering the boundary Note that the zeroth-order coefficient or “DC term” C0 curvature data in the active-contour model. extracts information about the average

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